Neural Networks are mainly trained using a variant of Stochastic Gradient Descent (SGD) such as normal SGD, SGD with momentum or Adam. The performance of these algorithms depends on the selected learning rate which would be different for different context and applications. An automatic framework based on RL for deriving the best learning rate is proposed in [1]}. In this approach, a set of features are introduced to represent the states in the RL modeling. These features include the variance and the gradient of the loss function. The state representation is used to train a policy using Relative Entropy Policy Search (REPS) algorithm [2]} for deciding the learning rate of a particular optimizer. REPS ensures the policy updates to be close to each other by constraining the updates through a bound on Kullback-Leibler (KL) divergence.
